Course information

This course is designed as a lecture course covering various topics in Statistical analysis (see below). I assume
students have some modest background in statistics and we build on this by discussing a
number of topics.
The goal of this course is to provide students with a better feel for statistics and to be much less intimidated by methods of statistical analysis.

Course Objectives: We will introduce statistical distributions and computing the statistical power of various designs, matrix algebra useful for statistics and the general linear model, maximum likelihood estimation and testing, Bayesian Statistics, and various resampling and randomization methods. The focus is obtaining a general understanding of these statistical tools rather than which computer programs to use. Thus, the course will be
somewhat more theoretical than applied, but the student will leave with a much broader understanding than a course concerned with running various statistical packages.

Math/Stats background required: Some knowledge of Calculus and a previous stats course (which introduced covariance, regression and ANOVA) is desirable.

Computer Programs: While the course focus is in basic statistical concepts, we will also introduce two programs:

JMP:
A cheap, easy to use graphical stats package that does most of the basics in an easy manner.

R: The most powerful and flexible statistical program, with a very large (and growing) library. Bad news: a little hard to get started on. good news: FREE!! (This is essentially S+, for those of you who have heard of this). More details are given below.

A draft of the R language definition (approx. 60 pages, 380kB) which document the language per se. That is, the objects that it works on, and the details of the expression evaluation process, which are useful to know when programming R functions.